In most manufacturing processes, management of through-put and yield are of concern. The ability to locate potential problems, identify problems, and take corrective action to obviate the source of the defect, and if possible, to repair the defect, can make a significant difference in the performance of manufacturing process. Therefore, it is desirable to have the best systems possible for identifying possible problems or anomalies, identifying an anomaly as a particular type of defect, identifying the source of the defect, and repairing the manufactured object to correct the defect if possible. This is particularly true in the semiconductor industry.
In the semiconductor manufacturing industry, a challenge remains to improve yields as the designs get smaller and smaller. Particles and process defects can limit yields in manufacturing semiconductor devices. Therefore, systems that perform the general functions described above can become extremely important. Conventional techniques have shortcomings including less than desirable speed and accuracy. With respect to identifying defects in the manufacturing process, manual classification has been required of anomalies and manual diagnosing of the cause of defects. Such manual inputs may have resulted in inconsistent results and consumption of considerable operator time.